Conventional visual models concerned with image fidelity focus on establishing a function over the visual features of two images to establish how similar they are, or how closely they appeal to the human eye. While simple heuristics such as mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are easy to compute and integrate in optimization scenarios, they have been largely abandoned in favor of quality assessment—of high-quality images.
Some newer, sophisticated models try to focus on combining feature statistics since standard techniques such as MSE and PSNR have not been matched well to perceived visual quality. Some of the main trends in the image quality arts are at an intersection between cognitive sciences understanding the workings of the visual cortex—and adhoc heuristics. For example, one class of algorithms separates images into subbands (channels) that are selective for spatial and temporal frequency and orientation. Sophisticated channel decompositions analyze the neural responses in the primary visual cortex of the brain.
Alternatively, many metrics try to use simpler transforms such as the DCT or separable wavelet transforms to achieve the same goal. Channel decompositions based upon temporal frequencies have also been used for video quality assessment.
One of the most difficult aspects of the problem is to arrive at its definition: what constitutes a hi-fidelity image? Once resolved, analytical progress is likely to follow rapidly. For example, certain distortions may be visible but not considered of poor fidelity suggesting that the correlation between image fidelity and perceived visual quality is arguable or subjective. One approach combines several visual models that are typically mixed using the Minkowski norm, which inherently assumes spatial independence. Thus, visual masking models have been proposed to account for the interdependence of image coefficients.
What is needed is a visual perception model that allows an image to be altered while reliably maintaining the human perception of hi-fidelity in the image. At the same time the visual perception model would lend itself to broad practical application via speedy and simple computations.